{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f79b89d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams['font.sans-serif']=['SimHei']\n",
    "plt.rcParams['axes.unicode_minus']=False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1fb5d51a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>区域</th>\n",
       "      <th>销量</th>\n",
       "      <th>占位1</th>\n",
       "      <th>同比去年</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>华北</td>\n",
       "      <td>4321</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>华南</td>\n",
       "      <td>1946</td>\n",
       "      <td>2375</td>\n",
       "      <td>-0.208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>东北</td>\n",
       "      <td>1536</td>\n",
       "      <td>2785</td>\n",
       "      <td>-0.093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>西北</td>\n",
       "      <td>1872</td>\n",
       "      <td>2449</td>\n",
       "      <td>-0.159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>西南</td>\n",
       "      <td>1369</td>\n",
       "      <td>2952</td>\n",
       "      <td>-0.179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>华东</td>\n",
       "      <td>2109</td>\n",
       "      <td>2212</td>\n",
       "      <td>-0.058</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   区域    销量   占位1   同比去年\n",
       "0  华北  4321     0 -0.136\n",
       "1  华南  1946  2375 -0.208\n",
       "2  东北  1536  2785 -0.093\n",
       "3  西北  1872  2449 -0.159\n",
       "4  西南  1369  2952 -0.179\n",
       "5  华东  2109  2212 -0.058"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=pd.read_excel('第二章 图表(前15).xlsx',sheet_name='8 数值百分比',usecols=\"B,C,D,F\",skiprows=1)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "866fbc0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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lypTJs110dLQmTpyY6/FtNpuGDh0qj8cTsLz00ku5zngoSa+99ppatWqlu+++W4ZhaMiQIWrWrJkMw1DXrl3Vs2dPGYZxwVsCAeByY4QKAC6jd999V2vXrtXq1av9EwmULl1aR48eDWh3+vRp/zNV2QzDULdu3WS32/XBBx9c0nkzMjJktVpls9kuOJqVzePxyOVyyWazyefz6ZFHHtGaNWsUExOjRo0aae7cubrjjjskyf9S11deeUXPPfeclixZoi1btujf//63//bAvLhcLnXs2FG7d+/Wxo0btWbNGvXu3Vu///67FixYcEnvuZKyvqfKlSsrMTFRq1at0pgxYwK2Dx06VN98840yMjK0fft2vfPOO6pbt65WrVolKWs06Y+3YKakpGjhwoU5zhMWFqaMjIyAkb7ff/9dISEh2rdvX0DbvEKEJP3rX/9S69at1bhxY7Vr105PPvmkli9frpCQEM2cOVMHDhzI9TmxP7NYLLLb7f5p7rPfm5XfuQtLfn3sj4Et2/bt2/XJJ58EPJN29OhRRqMAXBUIVABwmSxfvlxvvPGGRo8erQYNGvjX33333Vq7dq0GDhzoX7d58+Ycs769+eabWrp0qZYvX66SJUte0rmbNGmi9evXy2KxyGazaeXKlQG3aTmdTj3zzDPq06ePf53P55PH49G//vUvdenSRS+99JLeeOMN3XvvvapWrZq++eabfM/pdDr9YSwvR44cUZcuXbRv3z6tWLFC1apVU7Vq1RQTE6O///3v6tSpk7755ptcf6AbhqHDhw8rMzNTI0eO1FtvvSWn06nMzEz97W9/09ChQ3X69Gk99NBDAft9+umn+vTTT/0Bpm/fvgHb77zzzhy3/OV1fcWKFdPQoUP96z755BNFRkbq73//u39d9+7d83xeaO/evfrmm2+0bds2SVmz5t19993q37+/Bg8erP79+2vkyJH+d2vlJ6/v2e12X3DfP0pJSckR5i+V1WrVW2+9pXfeeSdgvcfjUZcuXXK079u3r+69996A723NmjV65pln/lIdAHA5cMsfAFwGCQkJateunVq2bKlXXnklYFu7du20YMEC/frrr5KyfsDPnz9fDzzwgL/NzJkzNXLkSL333nu69957L/n8a9askdfrlc/nk9PplMPhCFhiY2P18ccfB6xzuVzyer3q1KmTpKxnYxo3bqyQkBCFhYVp8uTJMgwjx7JixQpJWSM4Foslz1sMx40bp1tvvVXnzp3T+vXrA55zatu2rf75z39q9+7dOZ4ly74VsG7dupo0aZLWr1/vfzGuYRhq3ry57r//fh09elQtW7ZUZGRkvt/Nn6fpbtasWcAtezfffHOu+9WrV08TJkwIuKUte5bEP677+OOPVaVKlRz7G4ahfv36qU2bNqpRo4akrOe7vvvuO3355ZeqVauWbrvtNr300kv51p/N4XDo2WeflcVikcViUVxcnKSsZ5MuxunTp/Xyyy+rcuXKSk9Pz7XNyZMn8wyHfzZ06NAc/Sy3a/nkk0/0008/+V/0K2VN2rJ//3499thjuR47e/QNAK4EjFABQCFzu91q166dLBaL+vXrp82bN/u3Va5cWW3atFHdunXVuHFjtWrVSj/++KPi4uL8/+/8vn379PTTT+uuu+7Svffe65/SW5Jq1qx5wWdrpNxvs7oY2T/OL/V4F/OcVpMmTXT69GkNHz481+e1evXqpfbt2wdM2NG3b1999dVXio+PV+/evdWzZ09JWbP0zZ8/X3PmzNGIESP8s8NlfzfvvPOOevTokeM9XTt37lS9evX00UcfqUmTJpKkH3/8MWCE6sCBA7rpppty1Ne7d28lJycHfBfHjh1TaGioDh486F/ndDq1c+dOTZgwIWD/9957T+vWrdPOnTsD1h89elRRUVE6fPiwzp49q//93/9Vo0aN8v4i/9/PP/+ca9jJ791kDodD6enpev311zVlyhQVL15co0ePzvWfhyS9//77+uSTT7R79+4cMyX+2ZtvvhlwO6SUFSKffPJJ/+dDhw5p0KBBeu655/wTZxw+fFi9evVS586dVbVq1VyPff/99+uhhx7SiBEj8q0BAC4HAhUAFLIdO3Zo165dkuT/0Z7t888/V7du3bRkyRKNHDlSS5Ys0f333693333X/4P1hx9+UGZmptatW6d69eoF7J893fdf5fV6/bPKXayzZ8/6n536oxMnTuR5LI/H43/30y233KJhw4bJMIw8RxwiIiJ07tw5hYSEKCIiQpUqVdIHH3ygDh06yGq16uOPP9Y777yjgwcP6qGHHtILL7yg2NhYdejQQc8++6w++eQTValSRaNGjVL9+vX94cLtduvAgQNau3athg0b5p/K3Ov1Bpzf4XD4A8+fbzucOXOmihcv7n9mSZK6devmnzExm9vtzhF03nnnHb3++uv6+uuvVbZsWWVmZuqHH37QtGnTtGrVKg0YMED9+/fXqFGj1KRJE9WuXVvt27fXY489FjCS90cXui3wz9cmSStWrFBCQoKOHTumt956S7169VJ4eLhcLpcsFot++OEH/3EdDoe++uor3XjjjTnC1J/7j2EYGjZsWI7A8+abbwbMROh0OvXAAw9o1KhRMgxD3377rV544QXFxcVpypQp/nahoaHatm2bdu3apbNnz2r79u15zj4IAJedAQAo8ooXL258/PHHF93+5ptvNiTlu7jd7hz79ezZ84L75bZMmDAh1zq2bNlijBo1yjhy5IhhGIYxf/58IzY21ujatathGIbx2WefGTabzWjZsmXAfpmZmcYjjzxiLF++PGB9eHi4sWLFCv/nESNGGBaLxWjatKnh8/kMwzCMxMREIyoqyqhYsaJx8803Byw33HCDUbZs2RzrK1SoYERGRhpnz541vF6v0bZtW+O9994zXC6X0bp1ayM8PNwoU6aM8eKLLxpJSUkBNW3evNl49NFHjZCQEKNSpUpGamqqYRiG8dprrxkVK1a84D+r3bt3G126dDEqVKhglC1bNmDbZ599ZgwZMsQ4f/58jv369Olj2O32gH8O5cqVM9auXZuj7R133GGMGzfO/7l169bG8OHDA9ps27bNaNKkidGzZ89c63zyyScNSUa7du2MM2fOBGxbuHChUa1aNaNkyZJGTEyM0axZM//3AADBZjGMS/y/JAEA1xy73a5//OMfOSZoyMuRI0cUFRV1weeT/irDMPyjOxdz22JKSor+9a9/aeDAgf4RpZSUFEVHR1/ybIGSdP78ebnd7kueevxCPB6Pf/a9pUuXym63q2HDhvnOjnfw4EGdOnVKf/vb3yRJffr00aJFi/KcNOOP5ypZsqRKliypl1566aKfyboUdevWVefOndW/f39JUvPmzXX33XcHjFDNmzdPgwcP1uTJk9W0adMcx0hOTtaxY8dMvw4AAIKFQAUAwDXujwEOAFCwCFQAAAAAYBLTpgMAAACASQQqAAAAADCJQAUAAAAAJvGEqiSfz6ejR48qMjIy1xdYAgAAACgaDMNQenq6ypYtm+/sq9kIVMp6K3358uWDXQYAAACAK8Rvv/2mG2+88YLtCFSS/z0qv/32W463vwMAAAAoOtLS0lS+fPmLftcigUry3+YXFRVFoAIAAABw0Y8CMSkFAAAAAJhEoAIAAAAAkwhUAAAAAGASz1BdAq/XK7fbHewyEEQ2m00hISHBLgMAAABXCALVRTAMQ8ePH9fZs2eDXQquADExMbrhhht4ZxkAAAAIVBcjO0yVLl1axYoV44d0EWUYhjIyMvT7779LksqUKRPkigAAABBsBKoL8Hq9/jAVGxsb7HIQZBEREZKk33//XaVLl+b2PwAAgCKOSSkuIPuZqWLFigW5ElwpsvsCz9MBAACAQHWRuM0P2egLAAAAyMYtf9e4OXPm6Ntvv9Xs2bNltVqVmpqqEiVKKCQkRDabTSdPnlRUVJRSU1MVExMjSRo8eLBmz56tihUr5nrMn3/+Wfv27VOFChUu45UAAAAAVx4ClUnvbDl5Wc/3Wu1Sl7yPYRgaNWqU2rdvL6/XK4/Hoy5duuiBBx5Qv379FBERIbvdrnnz5mnQoEFKSEhQWFiYIiIi5PP55PF48jx29rNEAAAAQFFGoLqGDRs2TImJiZo0aZLGjx+vYcOGacyYMbr33nvVpUsXWa1WuVwuvfHGGxo2bJjCwsIkZU3E8fDDD6tbt265Hrd169b5hi0AAACgqCBQXaM2btyoDz74QBs2bFDNmjX9671er5YtWya73S5Jcjqdeu+999SoUSN/m4YNG2r16tX64Ycfcj12r169FB4eXrgXAAAAAFwFCFTXqHr16mnChAlq1aqVQkJC5HA4tHbtWh0/flzNmzdXvXr1VLduXXXs2FFHjhxRrVq11KxZM40dO1ZRUVEqUaJEnpMv+Hw+LVy4UJGRkVq5cuXlvTAAAADgCkKguoZ17txZ5cqV0zvvvKPvv/9edrtdZ86cUY0aNbR06VJ/uw8//FA///yzevTooR49eujHH3/U3r178z1227ZtebEtAAAAijwC1TVs4cKF2rx5s37//XfNnTtXN9xwg0qWLKnt27frtttu87c7c+aMHnzwQf/n2bNny2KxqFWrVpKkMWPGqHnz5qpdu7YkqW/fvqpfvz6BCgAAAEUegeoaduTIER0/flyZmZlKSkqSxWJRyZIlVbNmTa1bt87f7sMPPwz4bLVatXz5cu3Zs0eSlJiYqG+//VbLli2TJJ0+fVpWK68wAwAAAAhU17A+ffpo5cqVSkxM1MCBAxUeHq6tW7decISqWrVqslqtKl26tCTpxIkTqlmzpuLj4yVJcXFxTEoBAAAAiEAVYPy2Uwov4QpYV8xw684Qr37PdMvmCwlSZdKxDLep/U45PDqTlq4HH26h5wcMUmypUqpx2+36YeX/+ttM/3iatmzcoGMZbrlcLs2c87VsYWH+SSlOnDypX7ZuU2LyQUmS1+PRd4uXKqZilb9+YVcht9OtVJdXHyWcUYbFFuxygALz2q64YJcAAFcty76COY4x3CiYA+GyIVBd4w4fOqhfNm5Q5+49Vf/e+7QnYWfAdrfbraOHf5P+PzzZbDZ9NucbxZW+XqGhWd2j+9/b6qmevXR/s4ckSZmZmZf3IgAAAIArFIHKpK43Rwe7hItyXWysnh8wSENGvS1JcjgcAdvHDH9DX0z7p96eMFknUlLU8dEWioyKCnhGKvnAAY0Z8YamjB/rX+d0OHRrzVp69/0PLs+FAAAAAFcgi2EYRX5cMS0tTdHR0Rq++oDCS0QGbMu65e+UylaoKJv92ntuKCMjQ6GhoQoLCwt2KVcNt9Oho4cOaoM3llv+cE3hlj8AMI9b/q4d2dkgNTVVUVFRF2zPCFURV6xYsWCXAAAAAFy1mPsaAAAAAEwiUAEAAACASQQqAAAAADCJQAUAAAAAJhGoAAAAAMAkAhWuCH919v61q1Zq6sTxF93e5XL9pfMBAAAAEoHqmtarSweVLR52UcuwlwdKkv7x9ij1fqpTwHGOHP5NZYuH6fy5c5d0/ub33K1PPph8UW0/nzZVzRrcqbNnzlywrdfrzRHAIqOiNHbUCJ05fTpgvWEY8nq9Ofa/v+4dWr54kSRp5ZIfdc8dt15UnQAAAMAf8R4qk8qOvbwvwj368qWPqISG2tTvldfU79XX82034tVBKhFZQpIUHh6h8D+9wNgeZs86nu2/L7FNS01VVHR0vse1h4crMir/NpKUmZmpqRPH6/G/d1DMddfl2O71euV2u2W322WxWPT2sCH6fNpU2e12WUNC5MjMlMViUZjdrjrVKstqtcoeHi63yyWnw6HnBwzSK8NG+o/3w9xv5Xa7dc/9TSRJYXa7bKH/vTaPx6NjRw6rfMVKF6wdAAAARRuB6hpmtVoVEhoqi8WikJAQhYYG/uP2er3yeDyyhoTIGhLi38disQS0s4VlhceQ/2/zy8YNeqZzBw0e8abadeoiSTp29Iia3vk3RRQr7m93IuW4Evfu0T/eHiWvx6PMzAw92fMZDR75VsDxx789SulpqVq2eJGWLV4khyNTvyUnq2r1GpIkt9slt8utbxYtUdlyN+qN0e/ojdHv6Ie53+qhlq01/NVBKhkbq0FDhun1Af1UMjZWzzz/os6dS1e5G8sHnMvn82nKP8bqqad7KSws91D8zwnjNOOzT7Vm6w7Z7fZL/t4BAABQdBCoioDJ497VxHfezhGUDMPQS6/lPXrl8/nkcrn8t9edPnlSU/4xVjOnf6aezz6vh1u38bctU7acEg6nBOz/6AON1alrD/39yafyPMf/rlyhae9P1Mx5C3RP4/slSTu2bVXXdo9p6bpNee73439+0IvP9ND8Zaty3T7t/Qn6+qsZmvPDQt1UtZp//fSPp2nn9m16fsCgXPdbvOB7TfnHWM2Y+z1hCgAAABfEM1RFwKAhw3Q43aHf0jIDlsPpDg0aMixH+6+/+lJli4fpxshwxcdG6fChg5KkxnVr6dy5dK3cvE2DR76l4iVK/KW6En7drmc6/119+vX3h6k/yn7+yeFwyOl0+tfv2vGr+j3TQ+OnfqTbat0R8DyV4fPJ5/Np0NDhqn/PvWr/cDMl7U+UJP26dYveHTlMFSpVzrWe+d/+Wy8+3V2fzvq37qzf4C9dGwAAAIoGRqiKiMnj3tOs6Z8HrHv2pQF6suczOdq27dBJYz/4UF6PRw6Hwz+ytXjt+lyfK3K5XAoJCfHf6pef7OehwsLCVKFSZQ17+z1Vio9X5ZKRuqnazZIkhyNTJ0/8rgfvrivDMOT2uNXxqe569qUBkiS326NBQ4erTfu/S5KcDofc/z9rX2Zmpoo5HLJarZow7RO9P/YdlYorLUka/NILen7Ay/pl4/ocdR06mKwhA17Sv775Tnc1vOeC1wEAAABIBKoi4/y5dD3arr1eHf6mJGnYywNzzH6XLSQkROHhWRNTFC9RQqlnz0qSypS7Mdf2g196QSuXLgkIVOfS01S8RKT+8fYo/ePtUf71Xo9HbrdbXy9YpOq33qaOXbtp2y+bdWPFiv5b/Py3/K3fnOv5ataurVtuv11Op1NhYWF66x8TZbVmDba+PeF9/59DQ0M1YPBQ/37Tv/lOJWNj1aNDO/+6M6dP66MpkxQeHq55y1YqvkrVvL9EAAAA4E8IVEWEYRg5nqHyuN0Fcux//HNajnVPPPKQSt9wg6Z8Nj3P/Twej0JCQmSxXNydpx6Pxz+xxk+rV2nAs710+uQJlYiMUonISH87p8OhY0ePaMiotwOelYotVcr/Z5/Pp1nTv9DYUSNUvlIllb7+BsIUAAAALhmBqohwOpz616cf6V+ffixJyjx/Xi+8/Gq++3i9Xs3+1xeqf+99l3SuuV/P1u6EnXp38gfyer3q/kRbtevUWa3btg9o93yPp7T9l19kD8+atvyBu+tK+u8tf9mfpayZ/q4rGavvlqyQJN3XpKk27dmvH//zg957c4T+Z/EyRUVHy+1266m2bXRXw3v0XP+Bedbocrr0n3lzNXr8REXHXKchA/pd0jUCAAAAEoGqyBjx7liNeHfsRbdf9MN8jX1zhM6dO6fb7/hbju0Lv5+npMTEHKHll40bNHRQf834n/mqfFMVSVLnHj31XLcnVfr6G3T3Pff6207718xcz30xs/xla/ZISx0/elSPP9RUYyZO1thRIxUdE61JH3+WY0Tuj8IjwvXl/8yTlDXa9WffzvpKlW6qojp33nXBGgAAAFB0McvfNczj9eS73eVyaeXSJdq5fZv/uaOtv2zSt7NnaujA/urW+1n977adiq+SFYwOJh2QlHW73L+/+lLr/ndNwPFW/LhYPf7eTlO/mKEKlSor4dftWrZooY4fPapKlePV/e9tlfDr9gvW7fP58ny+KzfNW7ZW6etv0KNNG+lQcpIGvv6GbH94CfGfeb1e+Xw+/+eQkBD9nnJcaampkiS3260P35+oRd/Pu+gaAAAAUDQxQmXS0ZddwS7hgtLOpua7PSwsTEMHvSSX06WGjbKmLX+oRSvdVLWaXnz5Nf/EFDabTQ80f0RN7/ybP+iUiiutz+Z84z/WiZQUdW3/mEJCQvTi091V6aabVCn+JpWvWFHlbqygYWPe078+maYuj7XWys3bFBUdnWddmZmZcrvz/n53bt+mNSuWa9+e3dq2eZPOnTun9p276N33p+h/5sxS1/aP69y5dFWoWEllb7xRzw94WX+rd6d/f5cz60XB2arfepuiY67TreVv8K8rU+5GdezaPd/vDwAAALAYf3yJTxAYhiHDMPwjJJciNTVVBw8eVM2aNf9SDWlpaYqOjtbw1QcUXiIyYFsxw607Q06pbIWKstnD/9J5rkRer/eipju/GMsXL9JN1aqpYuX4XLefS0/XgcR9qlk75y2El+JQcpLeGTFMf6t3pxrc10i33J7zn3/Cr9u14eefdCg5WUNGjS6wa5Qkt9Oho4cOaoM3VhmWvEfCgKvNa7vigl0CAFy1LPsK5jjG8KD+NIf+mw1SU1MVFRV1wfZBH6FasGCBXn/9da1Zs0bR0dHq1KmTbrzxRr333nuSJIfD4R8p+bPp06dr4sSJSkhIyLMN8leQQaPJQ83z3V4iMvIvhylJqlCpsv75xZf5trnl9pq5Bi0AAACgIAX1GSqXy6VXX31Vjz32mKL//xYwm82mmJgYSdKiRYsUHx+vTZtyTk7gdDo1duxYTZw4kTAFAAAAICiCOkLVu3dvJSQkqHv3/z6rYrFYtH//frVv31579+7VlClTVKdOHUlSr1699PXXX0vKulXt3LlzeuqppwKO6fV65Xa7tWfPHlWsWPHyXQwAAACAIicoI1Rer1d9+vTRli1b/KNLbrdbP/zwg9avX68VK1aoTZs22rJlix5//HH/9NdTpkzRmTNntG/fPtntdi1atEinTp3SkSNHdPbsWZ09e1ZpaWk6ffq0KlSoULBFB/dRM1xJ6AsAAAD4f0EJVAcPHtSWLVs0b948/zM8PXr00JQpUxQTE6NevXqpc+fO/okqNm/eLClrVjqLxaLevXvrscce00MPPaSFCxcqPv6/kyBYLBYVK1Ys33cQOZ1OpaWlBSx5tlWIvIbkdjgK4tJxDXA7HPIaWX0DAAAARVtQbvmLj4/X+vXrJWW9c8jn82n69OmyWq3q0aOHkpOTZRiGLBaLjh49qvr162vx4sVq2LChXn31VW3fvl1bt26VJIWHhys09L+XYRiGXC6X7HZ7nucfM2aMRo4ceVG1ei1WHfZFyHbyhCTJFh4u5RPWcA0zDLkdDp06eUKHfRHyWniNGwAAQFEX9GnTq1atqv/85z+qWrWqpk6dqlOnTmnq1KlyOp1ZBVosql+/vr777julpqaqQoUKioyMVLFixSRlvbMoJSVFlSpVkpQ10YXValVycnKe53Q6nf7jS1lTI5YvXz7XadMlSYahykrTjdZMhZClijSvIR32RShJUQRrXHOYNh0AzGPa9GvHVTFtusfjkcfjUXh4uPbt+2/vmzFjhoYPH66hQ4f61/l8PjkcDlksFl133XVKTU2VYRj+WwVXrlypLl26KDEx8aLPb7fb8x3BysFiUZKidcgXKbu8F78frjlOhTAyBQAAAL+gBKpFixapdevWCgsLC1jvcrnUsmXLgJf8+nw+ud1unThxQqVKlZLVatWQIUOUkpKiTz75JGD/YcOGyev1avTo0YVSt9diVUZwZ5oHAAAAcAUJSjpo0aKFvF6vHA5HwFK/fn395z//CVjncrnk8XhUqlQpSdKsWbM0ceJE/f3vf89x3Mcee0zTpk3Tiy++eLkvCQAAAEARFJQRqrxm4DMMQz6fL8f67Nv7vvrqK/Xp00dz587Vgw8+KEk6e/as/3i1a9fW0qVL1bhxY0VEROjdd98tpCsAAAAAgCC/2PfP3G53wGQRf7Z582bNnj1bzZo1kyQ98sgjWrJkiTp27Ohvc8cdd+j7779XTExMYZcLAAAAoIgL+ix/f0VCQoKsVquqV6/+l46TPZNHnrP8AUARwCx/AGAes/xdO66KWf4Kyi233BLsEgAAAAAUYUxZBwAAAAAmEagAAAAAwCQCFQAAAACYRKACAAAAAJMIVAAAAABgEoEKAAAAAEwiUAEAAACASQQqAAAAADCJQAUAAAAAJhGoAAAAAMAkAhUAAAAAmESgAgAAAACTCFQAAAAAYBKBCgAAAABMIlABAAAAgEkEKgAAAAAwiUAFAAAAACYRqAAAAADAJAIVAAAAAJhEoAIAAAAAk0KDXcCVZECtWEVFRQW7DAAIjtpGsCsAgKsWf4MWXYxQAQAAAIBJBCoAAAAAMIlABQAAAAAmEagAAAAAwCQCFQAAAACYRKACAAAAAJMIVAAAAABgEoEKAAAAAEwiUAEAAACASQQqAAAAADCJQAUAAAAAJhGoAAAAAMAkAhUAAAAAmESgAgAAAACTQoNdwJVk/LZTCi/hCnYZwFXptV1xwS4BAICgsewL7vmN4UZwCyjCGKECAAAAAJMIVAAAAABgEoEKAAAAAEwiUAEAAACASQQqAAAAADCJQAUAAAAAJhGoAAAAAMAkAhUAAAAAmESgAgAAAACTCFQAAAAAYBKBCgAAAABMIlABAAAAgEkEKgAAAAAwiUAFAAAAACYRqAAAAADAJAIVAAAAAJhEoAIAAAAAkwhUAAAAAGASgQoAAAAATCJQAQAAAIBJBCoAAAAAMOmqD1SLFy/Wpk2bgl0GAAAAgCIoaIHK4XDI6/VesJ3P55PT6cxz+wcffKAVK1YUZGkAAAAAcFFCg3FSr9er6tWry263y2azSZL27dun+Ph4ud1uHT9+XJUqVZIkGYah6Ohovfvuu2rTpo2io6MVHh4uqzUrC+7fv1/bt2/X9OnT/cd3Op0qXry4tm7derkvDQAAAEAREpRAFRISouTk5IB11atX15o1a3T48GENGjRIS5cuDdju8/mUkpKi0ND/luxwOBQdHa1NmzapVKlSl6N0AAAAAPALSqAyw2q1qkmTJjp58qR/ncvlksfjUePGjXO0Hzt2rB5++OHLWCEAAACAoiaogapixYqSJJvNpkOHDunOO++Ux+PRyZMnVaVKFbndbsXGxuqXX36RJB08eFCTJ09Wy5YttWTJEt12220qU6aMJGnhwoVq1qyZQkJCVLduXfl8vqBdFwAAAICiIaiz/EVERGjZsmVKTExUyZIltW/fPs2fP18NGjRQYmKiFi9e7H/GSpKKFy/u//Pq1avVq1cvSdLevXvVrl077dq1y7/dbrfneV6n06m0tLSABQAAAAAuVVADVfbzUPv371dsbKxCQkJytLFYLDnap6amqn///qpfv74Mw9B7772nvn37qlq1ahc1c+CYMWMUHR3tX8qXL19AVwQAAACgKLki3kM1adIktWvXTlLWs1IpKSlau3atdu/enWv7cePG6Z577tHMmTMVFRWlxYsXa8GCBapRo4b/9sD8DB48WKmpqf7lt99+K9DrAQAAAFA0BH1SilWrVmn+/Pnatm2bJCk+Pl6xsbHq1q2bWrVqles+L774ooYPH67Q0FA1b95cXbp0UZcuXeTz+eTxeC54Trvdnu8tgQAAAABwMYIeqBo1aqTHH39c0dHRkqQSJUpo5cqVkqTdu3frp59+8rf1er1yOp2qVauWbDabLBaLUlJStHnzZg0dOlQ+n0/t27cPxmUAAAAAKIKCGqi8Xq8eeeQRhYWF5brd5XIpJibG/zkjI0N2u11Hjx71r/vjCFW2unXrFlrNAAAAAJAtqIHK4XBoyZIlqlKlSq7bd+/erQ4dOvg/Z2Rk5HoMp9MpKSuAzZgxQ+fPn1dsbGzhFA0AAAAA/89iGIYRrJOnpaWpRIkSslrNz42xe/duxcbGKi4uTpLkdrsDplq/2Dqio6M1fPUBhZeINF0LUJS9tisu2CUAABA0ln3BPb8xPGg/6a852dkgNTVVUVFRF2wf1BGqiynwQqpXrx7w+VLDFAAAAACYdUVMmw4AAAAAVyMCFQAAAACYRKACAAAAAJMIVAAAAABgEoEKAAAAAEwiUAEAAACASQQqAAAAADCJQAUAAAAAJhGoAAAAAMAkAhUAAAAAmESgAgAAAACTCFQAAAAAYBKBCgAAAABMIlABAAAAgEkEKgAAAAAwiUAFAAAAACYRqAAAAADAJAIVAAAAAJhEoAIAAAAAkwhUAAAAAGBSaLALuJIMqBWrqKioYJcBXJ1qG8GuAACAoOG/gkUXI1QAAAAAYBKBCgAAAABMIlABAAAAgEkEKgAAAAAwiUAFAAAAACYRqAAAAADAJAIVAAAAAJhEoAIAAAAAkwhUAAAAAGASgQoAAAAATCJQAQAAAIBJBCoAAAAAMIlABQAAAAAmEagAAAAAwKTQYBdwJRm/7ZTCS7iCXQaAIuK1XXHBLgEAUEAs+4JdQd6M4UawS7imMUIFAAAAACYRqAAAAADAJAIVAAAAAJhEoAIAAAAAkwhUAAAAAGASgQoAAAAATCJQAQAAAIBJBCoAAAAAMIlABQAAAAAmEagAAAAAwCQCFQAAAACYRKACAAAAAJMIVAAAAABgEoEKAAAAAEwiUAEAAACASQQqAAAAADCJQAUAAAAAJhGoAAAAAMAkAhUAAAAAmESgAgAAAACTCFQAAAAAYBKBCgAAAABMClqgcjgc8nq9F2zn8/nkdDpzrJ88ebI6dOggSZo1a5aaNGlS4DUCAAAAQH5Cg3FSr9er6tWry263y2azSZL27dun+Ph4ud1uHT9+XJUqVZIkGYah6Oho/fTTTwHHsNlsCg8PlyRFREQoLCzssl4DAAAAAAQlUIWEhCg5OTlgXfXq1bVmzRodPnxYgwYN0tKlS00f//z587JarYqIiPiLlQIAAABA3q6qZ6icTqcMw8i3TUJCgurVq6eBAwdepqoAAAAAFFVBGaHKVrFiRUlZt+8dOnRId955pzwej06ePKkqVarI7XYrNjZWv/zyiySpYcOGOnDggKxWqxwOh9q1a+c/Vlpamvr376+ZM2fqxRdfVL9+/YJyTQAAAACKjqCOUEVERGjZsmVKTExUyZIltW/fPs2fP18NGjRQYmKiFi9e7H/GSpI2bdqk06dP6+TJkxo3blzAsXbs2KHo6Gjt2bNHQ4YMUYkSJfI8r9PpVFpaWsACAAAAAJcqqIEqNDRrgGz//v2KjY1VSEhIjjYWi+WijtWgQQONGDFCMTExF2w7ZswYRUdH+5fy5ctfUt0AAAAAIF0hz1BNmjTJf/ue1WpVSkqK1q5dq927dxfK+QYPHqzU1FT/8ttvvxXKeQAAAABc24L6DJUkrVq1SvPnz9e2bdskSfHx8YqNjVW3bt3UqlWrQjmn3W6X3W4vlGMDAAAAKDqCPkLVqFEjbdmyRdHR0ZKkEiVKaOXKldq3b5969eoV0DY5OVmzZs1Sr169NHr06DyP6fP5NGfOHDkcjkKtHQAAAEDRFtQRKq/Xq0ceeSTPl/K6XC7/M1E///yz7rvvPj344INq0aKFSpYsqWPHjknKGnFKTExUamqqoqOjtWLFCj355JNKSkpSuXLlLtflAAAAAChighqoHA6HlixZoipVquS6fffu3erQoYMkqX79+jpy5IhKly4tSZowYYKSkpIkZU2nXqZMGVWpUkWGYchms+ntt98mTAEAAAAoVBbjQm/KLURpaWkqUaKErNbg3nmYlpam6OhoDV99QOElIoNaC4Ci47VdccEuAQBQQCz7gl1B3ozhQfu5f1XKzgapqamKioq6YPugjlBdTIEAAAAAcKUK+qQUAAAAAHC1IlABAAAAgEkEKgAAAAAwiUAFAAAAACYRqAAAAADAJAIVAAAAAJhEoAIAAAAAkwhUAAAAAGASgQoAAAAATCJQAQAAAIBJBCoAAAAAMIlABQAAAAAmEagAAAAAwCQCFQAAAACYRKACAAAAAJMIVAAAAABgEoEKAAAAAEwqlECVmJhYGIcFAAAAgCvKJQUqn8+nkydP6r777pMkbd++XQ6HI6CN2+1Wp06dVKdOHR0/frzgKgUAAACAK8xFByqfz6cHHnhAc+bM0a+//ipJevTRRxUTE6P77rtPI0eO1ObNm2Wz2TR27Fjt2rVL586dK7TCAQAAACDYQi+2odVq1dtvv627775bI0eOlCQlJSXp9OnT2r17t7755hs1a9ZMdrtdbrdb3333napUqVJohQMAAABAsF10oHI4HBozZoyKFy+u1NRUNW3aVGlpaUpLS9OxY8cUGxurxo0bq06dOvr444+1YcMGNWvWrDBrL3ADasUqKioq2GUAKCpqG8GuAABQQPgbvei66ECVmZmpe+65R+XKldPChQs1atQonTlzRqVKlVKpUqUUEhIiwzAUGxurxx57TB06dFB4eLgGDRpUmPUDAAAAQNBYDMO45EBdunRp/f7777r99tvl9XolSYZhKCwsTN988426deum6dOn6/z586pVq1aBF13Q0tLSFB0drdTUVEaoAAAAgCLsUrPBRY9Q/dEfM1hCQoL/zzVr1lTVqlXlcDhktVqvijAFAAAAAGZd8nuoXC6X3G63/3ODBg38S7ZHHnlEn376acFUCAAAAABXqEseofJ6vapatarOnDmjwYMHq2zZsrJYLJKkM2fOKDExUU2aNNHMmTMLvFgAAAAAuJJccqCKiIjQxo0bNXfuXO3YsUOdOnXybzt48KAaNGigAwcO6P777y/QQgEAAADgSnNJgcpqtSo0NGsXn88nSRo3bpx/u2EY8nq9+vLLL/X0008XYJkAAAAAcOW5pGeonE6nXC6XXC6X5syZo0GDBvk/Zz9btXLlSr377rtyuVyFVTMAAAAAXBEuaYTKZrP5/3zLLbeoZMmSOdrce++9GjFihNLS0lSqVKm/XiEAAAAAXKEuaYTqiSee8P+5Ro0aOZ6T8ng8slgs6ty5s2rVqiWPx1MwVQIAAADAFeiSAtXy5cslSfv3788Rlg4cOKCaNWvq+++/lySFhYX5n7cCAAAAgGvRJQUqqzWr+TPPPKMWLVro999/lyQlJiaqQYMGio+PV5MmTSTJP5U6AAAAAFyrLvnFvpL0wQcfKCIiQrfeeqs+/fRTPfTQQ2rQoIG+++47FS9evKBrBAAAAIArkql78mrUqKHvvvtOU6dO1TPPPKNKlSppzpw5V/0tfuO3nVJ4CWYnBK5Ur+2KC3YJAADkyrKvYI9nDDcK9oAoNKZGqLJt3LjRP/nEG2+8UVA1AQAAAMBVwfSQ0vvvv68lS5Zow4YNMgxDzZs3l8Vi0ZgxYwqyPgAAAAC4YpkKVL/88otee+01LVy4UGXKlJEk/fjjj6pbt66qV6+url27yjAYpgQAAABwbTN1y19UVJTeeustNWrUyL/uhhtu0Pjx4zVq1CjCFAAAAIAiwdQIVZUqVTRgwIAc65944gnVrl1bFouFadMBAAAAXPMuKVCdP39eb7755kW1PXv2rJl6AAAAAOCqcUmB6oknnlBKSor/Bb958fl8atOmzV+pCwAAAACueJcUqD7//PPCqgMAAAAArjp/6T1UAAAAAFCUEagAAAAAwCQCFQAAAACYRKACAAAAAJMIVAAAAABgEoEKAAAAAEwiUAEAAACASQQqAAAAADCJQAUAAAAAJhGoAAAAAMAkAhUAAAAAmESgAgAAAACTCFQAAAAAYBKBCgAAAABMumICldfr1U8//RTsMgAAAADgol0xgWrBggVq0aKFUlJSLqr95MmT1aFDB0nSrFmz1KRJk8IsDwAAAAByuGIC1VtvvaWRI0fq+uuvv6j2NptN4eHhkqSIiAiFhYUVZnkAAAAAkEPQAtXo0aMVHR2tmJgYRUdHa+PGjRo6dKhiYmL8S1RUlCIiIrR69epLOvb58+eVmZlZSJUDAAAAQJagBapXXnlFZ86c0fHjx3X99ddr2rRpOnv2rA4fPqyzZ8/q7NmzSktL0+nTp9WwYUNJktPplGEY+R43ISFB9erV08CBAy/HZQAAAAAowoIWqGw2m6xWqwYPHqxKlSqpV69eSkhIUGRkZMDoUkREhEJCQiRJDRs2VGxsrEqVKqVBgwYFHC8tLU39+/fX/fffr86dO+u9997L89xOp1NpaWkBCwAAAABcqtBgndjn82n8+PH68ssvtX37dknyPxMVGvrfspxOp+x2uyRp06ZN/vUffvih1q1b5/+8Y8cONWvWTHv27FFMTEy+5x4zZoxGjhxZUJcCAAAAoIgKaqCaNGmSQkJCdO+998piscjj8UiSatSoIUlyu93KyMjQnj17VLJkyXyP16BBA40YMeKizj148GANGDDA/zktLU3ly5c3dyEAAAAAiqygBarQ0FAdPHhQVqtVXq9XISEhSk5OVuXKlbV79+6AUaqCZrfb/aNeAAAAAGBWUKdNt1qt+vjjj9WiRQv/6FS2zz77TF27ds2xHgAAAACuFEENVCtXrtRLL72kTp065RiRatq0qdasWaMnnnhCbrdbkpScnKxZs2apV69eGj16dJ7H9fl8mjNnjhwOR6HWDwAAAKBoC1qgWrZsmR599FF98MEHeuqppyRJZ8+elSRZLBZVrFhRK1eu1KZNm9SlSxf9/PPPqlq1qr788kvVqlVLnTt39k+hbrfblZiYqNTUVEnSihUr9OSTT+rUqVNBuTYAAAAARUPQnqHaunWrxo0bp27dukmS+vTpo88//1z33nuvf5r0ChUqaPHixUpJSVH9+vV15MgRlS5dWpI0YcIEJSUlScqaTr1MmTKqUqWKDMOQzWbT22+/rXLlygXl2gAAAAAUDRbjQm/KvUySkpKUmpqqWrVqyWKxXNZzp6WlKTo6WsNXH1B4icjLem4AF++1XXHBLgEAgFxZ9hXs8YzhV8RP9CIpOxukpqYqKirqgu2DNkL1Z5UrVw52CQAAAABwSYI6KQUAAAAAXM0IVAAAAABgEoEKAAAAAEwiUAEAAACASQQqAAAAADCJQAUAAAAAJhGoAAAAAMAkAhUAAAAAmESgAgAAAACTCFQAAAAAYBKBCgAAAABMIlABAAAAgEkEKgAAAAAwiUAFAAAAACYRqAAAAADAJAIVAAAAAJhEoAIAAAAAkwhUAAAAAGASgQoAAAAATCJQAQAAAIBJocEu4EoyoFasoqKigl0GgLzUNoJdAQAAueK/UEUXI1QAAAAAYBKBCgAAAABMIlABAAAAgEkEKgAAAAAwiUAFAAAAACYRqAAAAADAJAIVAAAAAJhEoAIAAAAAkwhUAAAAAGASgQoAAAAATCJQAQAAAIBJBCoAAAAAMIlABQAAAAAmEagAAAAAwKTQYBdwJRm/7ZTCS7iCXQYA5PDarrhglwAAyIdlX7ArKDjGcCPYJVxVGKECAAAAAJMIVAAAAABgEoEKAAAAAEwiUAEAAACASQQqAAAAADCJQAUAAAAAJhGoAAAAAMAkAhUAAAAAmESgAgAAAACTCFQAAAAAYBKBCgAAAABMIlABAAAAgEkEKgAAAAAwiUAFAAAAACYRqAAAAADAJAIVAAAAAJhEoAIAAAAAkwhUAAAAAGASgQoAAAAATCJQAQAAAIBJBCoAAAAAMIlABQAAAAAmBT1QGYYhn89nev/Fixdr06ZNBVgRAAAAAFycoAeqBQsW6I477lBqaqokqVOnTnrllVf82x0OR777f/DBB1qxYkWh1ggAAAAAuQkN5sldLpdeffVVtWvXTtHR0ZIkm82mmJgYSdKiRYvUo0cPzZ8/X5mZmWrTpo2io6MVHh4uqzUrC+7fv1/bt2/X9OnT/cd1Op0qXry4tm7derkvCQAAAEAREtRA1bt3byUkJKh79+7+dRaLRfv371f79u21d+9eTZkyRXXq1JFhGEpJSVFo6H9Ldjgcio6O1qZNm1SqVKlgXAIAAACAIiwogcrr9er555/Xli1bFB4eLklyu91avHix1q9fL6fTqVGjRqljx47+kSiLxaImTZro5MmT/uO4XC55PB41btw4xznGjh2rhx9++LJcDwAAAICiKSiB6uDBg9qyZYvmzZunW2+9VZLUo0cPnThxQjExMXr00UfVuXNnf/vNmzerTp06OnjwoCZPnqyWLVtqyZIluu2221SmTBlJ0sKFC9WsWTOFhISobt26+U504XQ65XQ6/Z/T0tIK6UoBAAAAXMuCMilFfHy81q9fr4oVK8rn88nn82n69OlatGiRatSooeTkZBmGIUk6evSo6tevrxUrVqh48eL+Y6xevVq9evWSJO3du1ft2rXTrl27/Nvtdnue5x8zZoyio6P9S/ny5QvpSgEAAABcy4I+y1+5cuXk9XpltVo1depUxcfH6/vvv1dcXJxKlSqlWrVqqXnz5mrUqJH/+anU1FT1799f9evXl2EYeu+999S3b19Vq1ZNXq/3guccPHiwUlNT/ctvv/1W2JcJAAAA4BoUlFv+PB6PPB6PwsPDtW/fPv/6GTNmaPjw4Ro6dKh/nc/nk8PhkMVi8a8bN26cvv32W1mtVo0ZM0YxMTGKjo7WN998o9mzZ1/w/Ha7Pd8RLAAAAAC4GEEJVIsWLVLr1q0VFhYWsN7lcqlly5b+iSikrEDldrt14sQJ/7oXX3xRw4cPV2hoqJo3b64uXbqoS5cu8vl88ng8l+06AAAAABRtQQlULVq0kNfrDRh1kqSGDRtq5MiReuCBBwLWe71ehYSEyOv1yul0qlatWrLZbLJYLEpJSdHmzZs1dOhQ+Xw+tW/f/nJeCgAAAIAiLCiB6s9BKpthGLnOzhcSEiJJysjIkN1u19GjR/3b/jhCla1u3boFXDEAAAAA5BTUF/v+mdvtDpjO/M8yMjJyrHM4HP59XC6XZsyYofPnzys2NrbQ6gQAAAAASbIY2fOTX6V2796t2NhYxcXFScoKZTab7ZKOkZaWpujoaA1ffUDhJSILo0wA+Ete2xUX7BIAAPmw7Ltwm6uFMfyqjgd/WXY2SE1NVVRU1AXbX1EjVGZUr1494POlhikAAAAAMCvo76ECAAAAgKsVgQoAAAAATCJQAQAAAIBJBCoAAAAAMIlABQAAAAAmEagAAAAAwCQCFQAAAACYRKACAAAAAJMIVAAAAABgEoEKAAAAAEwiUAEAAACASQQqAAAAADCJQAUAAAAAJhGoAAAAAMAkAhUAAAAAmESgAgAAAACTCFQAAAAAYBKBCgAAAABMIlABAAAAgEkEKgAAAAAwKTTYBVxJBtSKVVRUVLDLAICcahvBrgAAkA/+li66GKECAAAAAJMIVAAAAABgEoEKAAAAAEwiUAEAAACASQQqAAAAADCJQAUAAAAAJhGoAAAAAMAkAhUAAAAAmESgAgAAAACTCFQAAAAAYBKBCgAAAABMIlABAAAAgEkEKgAAAAAwKTTYBVxJxm87pfASrmCXAQAAgKvM4PlxwS7hqmUMN4Jdwl/CCBUAAAAAmESgAgAAAACTCFQAAAAAYBKBCgAAAABMIlABAAAAgEkEKgAAAAAwiUAFAAAAACYRqAAAAADAJAIVAAAAAJhEoAIAAAAAkwhUAAAAAGASgQoAAAAATCJQAQAAAIBJBCoAAAAAMIlABQAAAAAmEagAAAAAwCQCFQAAAACYRKACAAAAAJMIVAAAAABgEoEKAAAAAEwiUAEAAACASQQqAAAAADAp6IHKMAz5fL5glwEAAAAAlyzogWrBggW64447lJqaKknq1KmTXnnlFf92h8OR636TJ09Whw4dJEmzZs1SkyZNCr9YAAAAAPiDoAYql8ulV199VY899piio6MlSTabTTExMZKkRYsWKT4+Xps2bcqxr81mU3h4uCQpIiJCYWFhl61uAAAAAJCk0GCevHfv3kpISFD37t396ywWi/bv36/27dtr7969mjJliurUqXNJxz1//rysVqsiIiIKumQAAAAA8AvKCJXX61WfPn20ZcsW/yiT2+3WDz/8oPXr12vFihVq06aNtmzZoscff1wWi0WS5HQ6ZRhGvsdOSEhQvXr1NHDgwEK/DgAAAABFW1BGqA4ePKgtW7Zo3rx5uvXWWyVJPXr00IkTJxQTE6NHH31UnTt39rffvHmz6tSpo4YNG+rAgQOyWq1yOBxq166dv01aWpr69++vmTNn6sUXX1S/fv3yPL/T6ZTT6QzYFwAAAAAuVVACVXx8vNavXy9J8vl88vl8mj59uqxWq3r06KHk5GQZhiGLxaKjR4+qfv36Wrx4ccCzVB9++KHWrVvn/7xjxw41a9ZMe/bs8T+DlZcxY8Zo5MiRhXJtAAAAAIqOoM/yV65cOXm9XlmtVk2dOlXx8fH6/vvvFRcXp1KlSqlWrVpq3ry5GjVqlO9xGjRooBEjRlwwTEnS4MGDlZqa6l9+++23AroaAAAAAEVJUEaoPB6PPB6PwsPDtW/fPv/6GTNmaPjw4Ro6dKh/nc/nk8Ph8D9HVRDsdrvsdnuBHQ8AAABA0RSUQLVo0SK1bt06x1TnLpdLLVu2lNX634Ezn88nt9utEydOqFSpUpe7VAAAAADIU1Bu+WvRooW8Xq8cDkfAUr9+ff3nP/8JWOdyueTxeFSqVCklJydr1qxZ6tWrl0aPHp3n8X0+n+bMmZPnS4EBAAAAoCAEJVBZLJZcb+EzDEM+ny/H+pCQEP3888+qWrWqvvzyS9WqVUudO3f2T6Fut9uVmJio1NRUSdKKFSv05JNP6tSpU4V7IQAAAACKtKC+2PfP3G53wHTmf1S/fn0dOXJEpUuXliRNmDBBSUlJkqSGDRuqTJkyqlKligzDkM1m09tvv61y5cpdttoBAAAAFD1XVKDauHFjvtuzw5Qk9e/f3//nqKgorVmzptDqAgAAAIDcBH3adAAAAAC4WhGoAAAAAMAkAhUAAAAAmESgAgAAAACTCFQAAAAAYBKBCgAAAABMIlABAAAAgEkEKgAAAAAwiUAFAAAAACYRqAAAAADAJAIVAAAAAJhEoAIAAAAAkwhUAAAAAGASgQoAAAAATCJQAQAAAIBJBCoAAAAAMIlABQAAAAAmEagAAAAAwCQCFQAAAACYRKACAAAAAJMIVAAAAABgksUwDCPYRQRbWlqaoqOjlZqaqqioqGCXAwAAACBILjUbMEIFAAAAACYRqAAAAADAJAIVAAAAAJhEoAIAAAAAkwhUAAAAAGASgQoAAAAATCJQAQAAAIBJBCoAAAAAMIlABQAAAAAmEagAAAAAwCQCFQAAAACYRKACAAAAAJMIVAAAAABgEoEKAAAAAEwiUAEAAACASQQqAAAAADCJQAUAAAAAJhGoAAAAAMAkAhUAAAAAmBQa7AKuBIZhSJLS0tKCXAkAAACAYMrOBNkZ4UIIVJJOnTolSSpfvnyQKwEAAABwJUhPT1d0dPQF2xGoJJUsWVKSdOjQoYv60oBLkZaWpvLly+u3335TVFRUsMvBNYg+hsJGH0Nhon+hsF1qHzMMQ+np6SpbtuxFHZ9AJclqzXqULDo6mn+RUWiioqLoXyhU9DEUNvoYChP9C4XtUvrYpQyyMCkFAAAAAJhEoAIAAAAAkwhUkux2u4YPHy673R7sUnANon+hsNHHUNjoYyhM9C8UtsLuYxbjYucDBAAAAAAEYIQKAAAAAEwiUAEAAACASQQqAAAAADCJQAUAQBF06tQp/fTTTzp58mSwSwGAq1qRD1Q7duxQvXr1dN111+nll18Wc3TgYp06dUqVK1dWcnKyf11+/cnsNhQ98+bNU3x8vEJDQ3XXXXdp165dkuhfKDizZ89WlSpV9Pzzz6tChQqaPXu2JPoYCl7z5s31xRdfSKJ/oeC88MILslgs/qVKlSqSgtfHinSgcjqdatWqlerUqaNNmzYpISHB/y89kJ+TJ0+qZcuWAWEqv/5kdhuKnv3796t79+565513dOTIEVWsWFFPP/00/QsF5uzZs3rhhRe0Zs0abdmyRdOmTdOrr75KH0OB++qrr7R48WJJ/DcSBWvz5s1asGCBzpw5ozNnzmjLli3B7WNGETZ37lzjuuuuM86fP28YhmFs3brVaNiwYZCrwtWgadOmxsSJEw1JRlJSkmEY+fcns9tQ9Hz//ffG1KlT/Z+XL19uhIWF0b9QYA4dOmTMmDHD/3nbtm1GZGQkfQwF6tSpU8b1119v3Hzzzcbnn39O/0KBcbvdRmRkpJGenh6wPph9LLQg0+LVZtu2bbr77rtVrFgxSVLNmjWVkJAQ5KpwNfjoo48UHx+vl156yb8uv/5kdhuKnpYtWwZ83rNnj6pUqUL/QoEpX768OnfuLElyu90aN26cHn/8cfoYCtTAgQP12GOPKTMzUxL/jUTB2b59uwzD0B133KEjR46oUaNG+uijj4Lax4r0LX9paWmqXLmy/7PFYlFISIjOnDkTxKpwNYiPj8+xLr/+ZHYbijaXy6Vx48bpueeeo3+hwG3btk3XX3+9fvzxR02cOJE+hgKzYsUKLVu2TO+++65/Hf0LBWXXrl269dZbNWvWLCUkJMhms6l3795B7WNFOlCFhobKbrcHrAsPD1dGRkaQKsLVLL/+ZHYbirahQ4eqRIkS6tWrF/0LBa5mzZpatmyZbr31VnXv3p0+hgLhcDjUu3dvTZ06VVFRUf719C8UlM6dO2vdunWqV6+eKleurClTpujHH3+Uz+cLWh8r0oGqZMmSOnHiRMC69PR0hYWFBakiXM3y609mt6HoWrJkiT788EPNnDlTNpuN/oUCZ7FYVLt2bX3xxReaN28efQwFYtSoUapXr55atGgRsJ7+hcISExMjn8+nG264IWh9rEgHqnr16mndunX+z8nJyXI6nSpZsmQQq8LVKr/+ZHYbiqYDBw6oc+fOmjp1qm655RZJ9C8UnOXLl+vll1/2fw4NzXqcunr16vQx/GUzZ87UvHnzFBMTo5iYGM2cOVPPPfecpk+fTv9CgRgwYIC+/vpr/+eNGzfKarXq9ttvD14f+yuzbFzt3G63ERcXZ0yfPt0wDMPo3bu30bJlyyBXhauJ/jDLX379yew2FD0ZGRlGjRo1jGeeecZIT0/3Ly6Xi/6FAnHkyBEjMjLSmDZtmnHo0CHjqaeeMh566CH+DkOB+O2334ykpCT/0rZtW2Ps2LHGiRMn6F8oENOnTzeqVKlirFq1yli2bJlRvXp1o0ePHkH9O6xIByrDyJoqMSIiwihdurQRGxtr7NixI9gl4Sryx0BlGPn3J7PbULTMnTvXkJRjSUpKon+hwCxatMioUaOGERkZabRr1874/fffDcPg7zAUvK5duxqff/65YRj0LxSc1157zYiJiTHKly9vvPjii8a5c+cMwwheH7MYBq+bPnLkiDZt2qQGDRooLi4u2OXgKpdffzK7DchG/0Jho4+hMNG/UNiC0ccIVAAAAABgUpGelAIAAAAA/goCFQAAAACYRKACAAAAAJMIVAAAAABgEoEKAAAAAEwKDXYBAAAUlrlz56ps2bJyOp167rnntGPHDg0YMECjR4/W+PHj5fV6NWzYsBz7DR48WLNnz1bFihVzPe7PP/+sffv2qUKFCoV9CQCAKxwjVACAa9YNN9ygjh07yu12y2azac2aNdq8ebMiIiK0fv36PN81EhERIZ/PJ4/Hk+uS3QYAAEaoAADXrJSUFPXs2VM7duzQ+fPntX79et17773au3evVq9erfHjx0uSDMOQ0+lUeHi4JMnr9erhhx9Wt27dcj1u69at/cEKAFC0EagAANeshQsX6ty5c5o1a5bi4uK0b98+eb1ezZ49W06nU82bN5fX69Xhw4f1wAMPaOHChZKkhg0bavXq1frhhx9yPW6vXr384QsAULRZDMMwgl0EAACF4ezZs+rYsaOioqJ04MABlSpVSuPHj9eECROUkZGhGTNmaPfu3WrTpo12796tzz77TGPHjlVUVJRKlCghi8WS63F9Pp9SU1MVGRmplStXXt6LAgBcURihAgBck44eParGjRtr0KBBqlWrlvr166chQ4ZozJgx+u6773TLLbdIko4dO6Zy5cpJknr06KEePXroxx9/1N69e/M9ftu2bVWmTJlCvw4AwJWNQAUAuCaVKVNGq1atUunSpVWnTh19/PHHqlevns6fP6+YmBh9++23ysjIUFJSkm6++eaAfWfPni2LxaJWrVpJksaMGaPmzZurdu3akqS+ffuqfv36BCoAAIEKAHBtOnz4sO6++2653W5lZmaqY8eOOn/+vEqWLKmdO3fqxIkTWrBggdasWaOmTZsG7Gu1WrV8+XLt2bNHkpSYmKhvv/1Wy5YtkySdPn1aVisT5QIACFQAgGtU+fLldeTIEXXs2FEtWrRQly5d1LdvX5UvX15S1u19r776qo4cOaJ33303YN9q1arJarWqdOnSkqQTJ06oZs2aio+PlyTFxcUxKQUAQBKTUgAArmEZGRnq2rWrdu/erRo1aug///mPEhIS/C/krVy5su644w7NnTvXv4/L5VLDhg0VFhbmn5Ri165dKlOmjGJiYiRJHo9HTz31lJ577rnLfk0AgCsLgQoAcM1bvny5OnfurFatWmnZsmXasGGDRo4cqQ0bNmj//v0aO3as/51ThmHo6NGjuv766xUamnUjR5s2bdSnTx81b95ckpSZmSmJl/sCAAhUAIBrlMfj0ccff6zZs2crLCxMkydPVvXq1fXtt99qyJAhuuuuuzRt2jT98ssvatGihTp37qw33nhDDz30kKKiogKekdq/f7/i4uIUFRXlX+dwOHTHHXfoww8/DMblAQCuEAQqAMA16+uvv9ZNN92kOnXq+Nf99NNPcrvdatSokX/dwYMHlZaWpttvvz0YZQIArmIEKgAAAAAwiTlfAQAAAMAkAhUAAAAAmESgAgAAAACTCFQAAAAAYBKBCgAAAABMIlABAAAAgEkEKgAAAAAwiUAFAAAAACb9H6KE14KqgemfAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "regions =data['区域'].values\n",
    "sales = data['销量'].values\n",
    "placeholder1 = data['占位1'].values\n",
    "year_on_year = data['同比去年'].values*100\n",
    "\n",
    "last_year_sales = [s / (1 - y / 100) for s, y in zip(sales, year_on_year)]\n",
    "increment = [s - l for s, l in zip(sales, last_year_sales)]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "ax.barh(regions, sales, label='销量', color='skyblue')\n",
    "ax.barh(regions, placeholder1, left=sales,color='orange')\n",
    "ax.barh(regions, increment, left=[s + p for s, p in zip(sales, placeholder1)], label='同比去年变化', color='green')\n",
    "\n",
    "ax.set_xlabel('数量')\n",
    "ax.set_ylabel('区域')\n",
    "ax.set_title('2021年各区域销量及同比情况')\n",
    "\n",
    "ax.legend()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
